High rates of head injury among homeless and low-income housed men: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To examine the predictors and temporal patterns of head injury (HI) presentation in the emergency department among cohorts of homeless and low-income housed men. METHODS: Retrospective review and logistic regression of HIs found in emergency department records for three groups of men, those: (1) who were chronically homeless with drinking problems (CHDP) (n=50), (2) in the general homeless population (GH) (n=60) and (3) in low-income housing (LIH) (n=59). RESULTS: The proportion of individuals with non-minimal HIs documented in the previous year were 28%, 3% and 5% with annual rates of 0.47, 0.017 and 0.037 among the CHDP, GH and LIH groups (p<0.0001). In the multivariate model, independent associations with having an HI included: an HI in the previous year (OR 11.8, 95% CI 3.83 to 36.4), drug dependence (OR 3.67, 95% CI 1.11 to 12.13) and seizures (OR 3.50, 95% CI 1.13 to 10.90), while mood-disorders were protective. Homelessness had a crude risk increase of HI (OR 3.15, 95% CI 1.21 to 8.23) but was not significant in the multivariate model. Among those with HIs, chronic homelessness with drinking problems was associated with a higher rate of HI. With each successive HI, the time interval to another HI was 12 days shorter (p=0.0004). The chronic subdural haematoma incidence in the under-65-year-old CHDP group was 11 per 1000 (95% CI 2.8 to 45). CONCLUSIONS: Having an HI is better predicted by previous head injuries, drug dependence or a seizure disorder than a history of homelessness or alcohol dependence. HIs may become more frequent with time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".